Authors:
Rohan Thomas Jepegnanam, V. Surya, A. T. Prabhakar, M. Mohamed Sameer Ali, S. Silvia Priscila
Addresses:
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Neurological Sciences, Christian Medical College (CMC), Vellore, Tamil Nadu, India. Department of Computer Science and Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India.
Abstract:
This study benchmarks predictive models in ROI-based lesion–symptom mapping (LSM) for aphasia and integrates SHapley Additive exPlanations (SHAP) to deliver transparent neurodiagnostic predictions. Lesion–ROI overlaps were calculated for nine language-related regions in 70 patients with aphasia. Seven machine learning models (XGBoost, SVR-Linear, SVR-RBF, Random Forest, Ridge, Lasso, and MLP) were trained to predict standardized behavioral scores. Model performance was assessed using R², RMSE, MAE, AUC, F1 score, sensitivity, and specificity. SHAP provided global and local feature attributions, which were statistically validated against Pearson’s r and two-sample t-statistics via Spearman’s ρ and 5,000-permutation testing. SVR-RBF achieved the highest predictive accuracy (R² = 0.927; RMSE = 1.227; AUC = 0.938), with SHAP consistently highlighting the Left Arcuate Fasciculus and Broca’s area as top contributors. Spearman correlations between mean |SHAP| and univariate metrics were ρ = 0.75 (p = 0.020; perm p = 0.025) for Pearson’s r and ρ = 0.72 (p = 0.030; perm p = 0.019) for t-statistics. Our SHAP-augmented ROI-based LSM framework predicts the severity of aphasia and provides explanations at a specific level, thereby increasing transparency and understanding. By elucidating how the AI arrives at its predictions, this approach enhances transparency, fosters clinician trust in AI-driven diagnostics, and guides personalized rehabilitation strategies.
Keywords: Lesion-Symptom Mapping; Region-of-Interest; Explainable AI; Machine Learning; Brain Lesions; SHapley Additive exPlanations; Explainable Artificial Intelligence; Random Forest.
Received: 30/06/2024, Revised: 12/10/2024, Accepted: 15/11/2024, Published: 03/03/2025
DOI: 10.64091/ATIHL.2025.000117
AVE Trends in Intelligent Health Letters, 2025 Vol. 2 No. 1 , Pages: 16-29